library(rstan)
library(survival)
library(tidyverse)
library(tidybayes)
# data, parameters, model and generated quantities blocks
Stan_exponential_survival_model <- "
data{
int <lower=1> N_uncensored;
int <lower=1> N_censored;
int <lower=0> numCovariates;
matrix[N_censored, numCovariates] X_censored;
matrix[N_uncensored, numCovariates] X_uncensored;
vector <lower=0>[N_censored] times_censored;
vector <lower=0>[N_uncensored] times_uncensored;
}
parameters{
vector[numCovariates] beta; //regression coefficients
real alpha; //intercept
}
model{
beta ~ normal(0,10); //prior on regression coefficients
alpha ~ normal(0,10); //prior on intercept
target += exponential_lpdf(times_uncensored | exp(alpha+X_uncensored * beta)); //log-likelihood part for uncensored times
target += exponential_lccdf(times_censored | exp(alpha+X_censored * beta)); //log-likelihood for censored times
}
generated quantities{
vector[N_uncensored] times_uncensored_sampled; //prediction of death
for(i in 1:N_uncensored) {
times_uncensored_sampled[i] = exponential_rng(exp(alpha+X_uncensored[i,]* beta));
}
}
"
# prepare the data
set.seed(42);
require (tidyverse);
data <- read_csv('../data.csv')
New names:
* `` -> ...1
Rows: 2066 Columns: 12
── Column specification ───────────────────────────────────────────────────────────────────────────────────────
Delimiter: ","
chr (3): url, host_type, project
dbl (8): ...1, major_releases, duration, author_count, rev_count, status, multi_repo, commits_per_day
lgl (1): commit_freq_above_median
ℹ Use `spec()` to retrieve the full column specification for this data.
ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
N <- nrow (data);
colnames(data)[which(names(data) == "commit_freq_above_median")] <- "high_freq"
data$high_freq <- car::recode(data$high_freq, "'TRUE' = 0; 'FALSE' = 1")
X <- as.matrix(pull(data, high_freq));
is_censored <- pull(data, status)==0;
times <- pull(data, duration);
msk_censored <- is_censored == 1;
N_censored <- sum(msk_censored);
# put data into a list for Stan
Stan_data <- list (N_uncensored = N - N_censored,
N_censored = N_censored,
numCovariates = ncol(X),
X_censored = as.matrix(X[msk_censored,]),
X_uncensored = as.matrix(X[!msk_censored ,]),
times_censored = times[msk_censored],
times_uncensored = times[!msk_censored])
# fit Stan model
require(rstan)
exp_surv_model_fit <- suppressMessages(stan(model_code = Stan_exponential_survival_model, data = Stan_data))
Warning in system(paste(CXX, ARGS), ignore.stdout = TRUE, ignore.stderr = TRUE) :
error in running command
clang: error: no input files
SAMPLING FOR MODEL 'bf5dbbde6a245330de71a285e3fe7c42' NOW (CHAIN 1).
Chain 1:
Chain 1: Gradient evaluation took 0.00038 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 3.8 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1:
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Chain 1:
SAMPLING FOR MODEL 'bf5dbbde6a245330de71a285e3fe7c42' NOW (CHAIN 2).
Chain 2:
Chain 2: Gradient evaluation took 0.000166 seconds
Chain 2: 1000 transitions using 10 leapfrog steps per transition would take 1.66 seconds.
Chain 2: Adjust your expectations accordingly!
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Chain 2:
SAMPLING FOR MODEL 'bf5dbbde6a245330de71a285e3fe7c42' NOW (CHAIN 3).
Chain 3:
Chain 3: Gradient evaluation took 0.000171 seconds
Chain 3: 1000 transitions using 10 leapfrog steps per transition would take 1.71 seconds.
Chain 3: Adjust your expectations accordingly!
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Chain 3:
SAMPLING FOR MODEL 'bf5dbbde6a245330de71a285e3fe7c42' NOW (CHAIN 4).
Chain 4:
Chain 4: Gradient evaluation took 0.000194 seconds
Chain 4: 1000 transitions using 10 leapfrog steps per transition would take 1.94 seconds.
Chain 4: Adjust your expectations accordingly!
Chain 4:
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# print model fit
print(get_seed(exp_surv_model_fit))
[1] 1781592037
# print fit summary
fit_summary <- summary(exp_surv_model_fit)
print(fit_summary$summary)
mean se_mean sd 2.5% 25% 50%
beta[1] -0.5627731 0.002051500 0.07849821 -0.7185476 -0.6156442 -0.5622044
alpha -4.7053300 0.001385949 0.05354377 -4.8135036 -4.7414188 -4.7046110
times_uncensored_sampled[1] 114.5263745 1.821339652 116.20953176 2.1146522 33.9126867 79.9736773
times_uncensored_sampled[2] 108.1134428 1.669845311 105.04229406 3.3082306 31.9875653 75.5178964
times_uncensored_sampled[3] 198.2410916 3.144339330 198.73890720 3.8884625 55.0230159 135.8153785
times_uncensored_sampled[4] 110.6483114 1.748954573 108.69457969 2.9578270 31.9239804 77.0404231
times_uncensored_sampled[5] 110.8150397 1.774953010 108.26286017 3.1555139 33.9398092 78.7307897
times_uncensored_sampled[6] 192.3124555 3.025133299 194.03377751 5.3754349 54.5858821 129.8933742
times_uncensored_sampled[7] 110.4297388 1.908294274 114.73922234 2.5394040 29.7074168 77.5391111
times_uncensored_sampled[8] 109.5966171 1.768562687 112.15323587 2.6537069 31.3291990 73.5612027
times_uncensored_sampled[9] 110.0911999 1.756423158 110.03112390 3.0187061 31.8232512 74.9396860
times_uncensored_sampled[10] 109.9939413 1.756583990 111.54308203 2.6627561 31.7422666 74.2301217
times_uncensored_sampled[11] 108.0187493 1.770858057 108.50192968 2.7915007 32.0544236 74.4241487
times_uncensored_sampled[12] 110.5245005 1.736415669 110.71848494 2.9914026 32.5554615 76.5090532
times_uncensored_sampled[13] 112.9162068 1.772189365 113.52095044 3.1299861 31.4018321 76.8538179
times_uncensored_sampled[14] 186.6070126 3.009718021 190.15873045 4.7652013 52.4184303 127.3520734
times_uncensored_sampled[15] 196.2323226 3.141083753 197.64262592 4.1662471 54.6850277 132.9723982
times_uncensored_sampled[16] 113.0754620 1.838293588 115.31869928 2.6663780 31.9207203 76.9207487
times_uncensored_sampled[17] 110.0234809 1.763211674 111.38828799 2.5408079 31.0041415 75.0049552
times_uncensored_sampled[18] 110.2099553 1.678901635 108.11420816 2.7162200 32.1855263 78.3886822
times_uncensored_sampled[19] 113.6374490 1.799516456 113.39152387 2.1226973 34.2365979 78.2821371
times_uncensored_sampled[20] 109.7046948 1.784999875 111.43566592 2.8095517 31.1294384 74.3680194
times_uncensored_sampled[21] 107.1621694 1.700989685 107.78229207 2.7316949 31.2003423 75.1161877
times_uncensored_sampled[22] 111.5307846 1.807296367 110.02902685 2.6380222 32.0740739 79.4585108
times_uncensored_sampled[23] 109.7564675 1.851334065 110.51000932 2.3302378 31.4596232 75.4311562
times_uncensored_sampled[24] 110.3380416 1.691599016 108.52366036 2.8875659 32.7641663 77.8362199
times_uncensored_sampled[25] 111.3387585 1.781248805 113.31858144 2.7620896 30.3301020 76.2134749
times_uncensored_sampled[26] 111.3020003 1.799731790 112.28836855 2.6169913 32.8450758 75.9414823
times_uncensored_sampled[27] 111.5768212 1.818555867 112.98596180 2.6092111 31.3010776 77.1494166
times_uncensored_sampled[28] 110.1185742 1.742310136 110.10975452 2.6420956 32.1703732 75.7350829
times_uncensored_sampled[29] 199.9052619 3.170949097 202.13583843 4.4764225 55.7346781 136.6155888
times_uncensored_sampled[30] 110.8966044 1.760556893 112.46014472 2.6548108 31.1138031 74.2863356
times_uncensored_sampled[31] 111.8069565 1.710141090 111.12915398 2.8444262 32.3653861 75.0249428
times_uncensored_sampled[32] 110.3347843 1.754607686 109.50101474 2.3626352 30.3844583 76.3061267
times_uncensored_sampled[33] 110.4732865 1.702497410 109.96368187 2.2211581 31.0841065 76.9794380
times_uncensored_sampled[34] 108.9838871 1.811134818 109.08468550 2.6841503 32.6238384 75.3325086
times_uncensored_sampled[35] 112.1110107 1.691647177 109.45638672 2.7120793 32.2584848 80.4234770
times_uncensored_sampled[36] 197.3236257 3.133386428 196.50690116 4.8286801 57.3932931 138.3078934
times_uncensored_sampled[37] 198.7881621 3.227680751 200.48430980 4.7757457 55.1949824 135.6173967
times_uncensored_sampled[38] 193.3519736 2.992925204 192.10140764 4.9713019 54.8870206 136.4986001
times_uncensored_sampled[39] 111.6041633 1.735598114 111.04532277 2.9470936 32.9211523 78.8363944
times_uncensored_sampled[40] 198.2285765 3.172411774 199.05200534 5.4593551 57.3709433 137.8305368
times_uncensored_sampled[41] 198.0499222 3.198066432 199.61965019 5.1013728 56.7165082 136.2629226
times_uncensored_sampled[42] 189.6244808 3.009094078 186.95841487 5.1943248 56.8035249 129.0151504
times_uncensored_sampled[43] 109.3335164 1.855588750 109.15671638 2.6468214 31.9087008 75.3574718
times_uncensored_sampled[44] 110.3631519 1.777750216 110.08715652 2.5820712 32.1916817 75.3665699
times_uncensored_sampled[45] 195.3372009 3.102399587 195.26204716 5.3476334 57.5212544 133.7463977
times_uncensored_sampled[46] 109.2836628 1.813945144 112.72642330 2.7900231 31.3280179 75.1503047
times_uncensored_sampled[47] 195.7074178 3.151071721 196.97531686 4.9410348 52.7104251 135.5520685
times_uncensored_sampled[48] 108.2731705 1.729221539 108.68364647 2.8680600 30.3723967 74.3242517
times_uncensored_sampled[49] 109.5471375 1.672776327 109.08240485 2.5505981 30.9331599 76.2364203
times_uncensored_sampled[50] 107.7838218 1.683408928 106.58577614 3.4239692 31.9165816 75.1780685
times_uncensored_sampled[51] 112.9284757 1.794551812 110.80418141 2.6309150 33.2734005 77.8626800
times_uncensored_sampled[52] 195.5230341 3.065063778 196.33660518 4.4256385 56.2145309 135.4578437
times_uncensored_sampled[53] 189.1881279 3.154967989 189.24924045 4.1070890 54.9357459 129.4628501
times_uncensored_sampled[54] 110.8698922 1.771884925 112.80592663 2.5762164 33.1198327 74.8076388
times_uncensored_sampled[55] 197.0692581 3.130246326 193.53590971 5.1111696 58.6161599 139.6874080
times_uncensored_sampled[56] 111.9859321 1.858310585 111.57399373 2.9828434 31.9792821 77.5187601
times_uncensored_sampled[57] 112.3565073 1.765208599 110.13158415 3.2518588 32.3872759 78.8510434
times_uncensored_sampled[58] 198.5731418 3.236329513 200.55140060 4.8884726 54.8635962 135.9055863
times_uncensored_sampled[59] 197.0131093 3.103526075 192.24459492 5.0497805 59.0448117 138.8798664
times_uncensored_sampled[60] 112.9511175 1.829230635 113.16397679 3.0370208 32.3713443 77.0118778
times_uncensored_sampled[61] 112.5459482 1.730343449 113.37611537 2.6734133 31.9992328 77.4659593
times_uncensored_sampled[62] 190.1537851 3.139313342 187.98272586 4.3450312 55.2266165 132.1019823
times_uncensored_sampled[63] 110.4392058 1.764243139 109.79809717 2.9731826 32.7995791 77.6213597
times_uncensored_sampled[64] 194.8143740 3.075940576 198.26387393 4.5875354 56.3749833 134.4837671
times_uncensored_sampled[65] 195.1939224 3.169885657 195.92340632 5.0078479 58.9701352 136.3159012
times_uncensored_sampled[66] 108.0587897 1.713615317 109.77044404 2.6836684 31.3205364 73.2563154
times_uncensored_sampled[67] 112.9125173 1.813717629 113.05824905 2.6170588 32.1563000 78.3873951
times_uncensored_sampled[68] 193.3883575 2.987271494 193.82240703 5.5605893 57.5109824 132.2883550
times_uncensored_sampled[69] 110.7901370 1.852771396 111.28096974 2.7329566 31.2563663 77.4522683
times_uncensored_sampled[70] 193.9051529 3.074031647 192.95722109 5.6653897 56.5439350 133.8992221
times_uncensored_sampled[71] 108.0550404 1.665360941 106.61177035 2.6025317 30.8195744 73.2645224
times_uncensored_sampled[72] 192.0605168 2.943853585 190.30410932 5.2334764 55.3034604 132.5029892
times_uncensored_sampled[73] 106.9616898 1.717349825 107.15448947 2.5836096 30.8345768 74.5055672
times_uncensored_sampled[74] 110.3850840 1.708965906 109.79443364 2.7402908 31.8043225 77.1152034
times_uncensored_sampled[75] 112.4601081 1.751805820 113.17809780 2.8237757 32.5866131 77.4002588
times_uncensored_sampled[76] 112.3728078 1.782821030 114.85923801 2.6604854 31.7591871 76.0423478
times_uncensored_sampled[77] 109.8077797 1.814980253 114.08883095 2.3144815 27.5580756 73.6727571
times_uncensored_sampled[78] 108.5342985 1.756699546 110.09148112 2.3985542 30.6574115 72.9389264
times_uncensored_sampled[79] 111.1941183 1.777138849 110.44994746 2.6967180 32.8870652 78.0399766
times_uncensored_sampled[80] 111.4391551 1.798038495 111.48540571 2.7982789 32.6480503 77.5253405
times_uncensored_sampled[81] 109.6729080 1.712414574 107.12634549 2.9906286 33.3792915 79.2478848
times_uncensored_sampled[82] 113.1907159 1.782675795 111.86554681 2.7539590 32.9230623 78.7693665
times_uncensored_sampled[83] 110.6905767 1.861117322 112.62311642 2.6201324 33.0517522 74.3652525
times_uncensored_sampled[84] 109.2696159 1.726568893 107.99236339 3.0831429 31.8313775 77.3217567
times_uncensored_sampled[85] 111.1374772 1.710844391 110.46872364 3.0176586 31.7096606 75.2640928
times_uncensored_sampled[86] 193.1838697 3.184533077 198.13646344 5.0545346 56.4280358 129.9965615
times_uncensored_sampled[87] 106.9349730 1.730559687 105.17234999 2.7496622 30.3533118 74.2710969
times_uncensored_sampled[88] 196.1561556 3.148901404 195.39696865 5.8014796 56.9950456 138.2400348
times_uncensored_sampled[89] 110.3082519 1.707172442 109.39114239 2.5125954 30.7431405 76.5523137
times_uncensored_sampled[90] 111.5968887 1.752408831 111.36411738 2.5043727 31.9454695 76.3181972
times_uncensored_sampled[91] 196.3470968 3.171881917 203.19167971 4.9790434 54.2039295 131.5912463
times_uncensored_sampled[92] 108.1853910 1.682194823 106.79060093 3.0732246 30.5940350 77.2905643
times_uncensored_sampled[93] 193.7856775 3.171041212 197.77650198 4.4147195 51.7858427 131.3225648
times_uncensored_sampled[94] 110.4495736 1.753802532 111.41869873 3.0301346 31.2247222 77.8784760
times_uncensored_sampled[95] 110.7470135 1.729248244 110.38309170 3.3899894 32.3240679 75.7008079
times_uncensored_sampled[96] 110.7178366 1.699295975 108.77867314 2.6173204 31.2850039 78.5609138
times_uncensored_sampled[97] 109.0123537 1.762037126 109.36560611 2.7747039 31.0490139 75.0323644
times_uncensored_sampled[98] 113.9142661 1.785220427 115.88348569 2.7179966 32.5015426 77.5602641
75% 97.5% n_eff Rhat
beta[1] -0.5092253 -0.4115704 1464.119 1.0009443
alpha -4.6692041 -4.6013848 1492.531 1.0011599
times_uncensored_sampled[1] 155.4433502 424.3496532 4071.005 0.9995632
times_uncensored_sampled[2] 151.8881272 395.6997102 3957.090 0.9993291
times_uncensored_sampled[3] 278.1175553 729.9058376 3994.910 1.0008087
times_uncensored_sampled[4] 154.1481990 393.5580246 3862.413 1.0010428
times_uncensored_sampled[5] 154.3172431 406.2820482 3720.363 0.9996140
times_uncensored_sampled[6] 264.8440126 703.0275355 4114.013 0.9996292
times_uncensored_sampled[7] 150.5434171 408.6841783 3615.207 1.0010495
times_uncensored_sampled[8] 151.8632870 407.9896851 4021.450 0.9993076
times_uncensored_sampled[9] 156.9014829 406.3392412 3924.396 0.9999069
times_uncensored_sampled[10] 152.9744232 421.1811082 4032.250 1.0007298
times_uncensored_sampled[11] 147.4231930 387.6331059 3754.114 1.0010512
times_uncensored_sampled[12] 149.5544778 422.0344833 4065.677 0.9996629
times_uncensored_sampled[13] 157.6876329 415.8911987 4103.286 1.0005536
times_uncensored_sampled[14] 260.5349191 708.4601394 3991.912 1.0002259
times_uncensored_sampled[15] 276.2602601 726.4860414 3959.152 0.9995366
times_uncensored_sampled[16] 155.0272396 423.5979617 3935.226 0.9996193
times_uncensored_sampled[17] 151.9372806 422.6505829 3990.894 0.9997893
times_uncensored_sampled[18] 152.6000666 394.9463872 4146.819 0.9992755
times_uncensored_sampled[19] 157.0345048 421.8296492 3970.540 0.9994436
times_uncensored_sampled[20] 151.0951222 410.8303972 3897.374 1.0003819
times_uncensored_sampled[21] 148.5991449 392.8766251 4015.055 1.0001694
times_uncensored_sampled[22] 152.0897357 412.3611987 3706.430 1.0003877
times_uncensored_sampled[23] 151.6794691 395.0498224 3563.146 1.0001554
times_uncensored_sampled[24] 150.8363580 408.7220831 4115.798 0.9999315
times_uncensored_sampled[25] 152.6573321 419.7620907 4047.185 1.0003869
times_uncensored_sampled[26] 154.8989244 406.9789286 3892.727 1.0011408
times_uncensored_sampled[27] 153.9437025 429.7686166 3860.074 1.0013753
times_uncensored_sampled[28] 150.7053289 418.4637498 3993.932 0.9995258
times_uncensored_sampled[29] 279.7315811 738.5971978 4063.573 0.9996468
times_uncensored_sampled[30] 154.7378905 405.3235714 4080.347 0.9999331
times_uncensored_sampled[31] 155.9961830 412.9657772 4222.719 0.9992341
times_uncensored_sampled[32] 157.0416669 399.2120414 3894.720 1.0001441
times_uncensored_sampled[33] 154.9406370 418.7628221 4171.821 0.9992427
times_uncensored_sampled[34] 148.0606470 400.8423140 3627.655 1.0002963
times_uncensored_sampled[35] 154.9768540 406.4531787 4186.611 0.9991918
times_uncensored_sampled[36] 273.4041940 713.5147580 3933.034 1.0007590
times_uncensored_sampled[37] 275.4321033 732.0878302 3858.155 1.0000280
times_uncensored_sampled[38] 267.1602531 703.8315365 4119.736 1.0000365
times_uncensored_sampled[39] 151.8933485 405.3149502 4093.570 1.0012436
times_uncensored_sampled[40] 273.6745963 740.1466759 3936.897 0.9999303
times_uncensored_sampled[41] 273.7756108 735.0003592 3896.114 0.9994488
times_uncensored_sampled[42] 266.0765392 696.5246611 3860.277 0.9998679
times_uncensored_sampled[43] 149.9054401 416.1613155 3460.489 0.9998084
times_uncensored_sampled[44] 155.0531047 412.0696433 3834.704 1.0003016
times_uncensored_sampled[45] 269.2200310 724.1449021 3961.323 0.9995756
times_uncensored_sampled[46] 148.9059910 423.5818554 3861.919 1.0001918
times_uncensored_sampled[47] 269.4891251 716.3331407 3907.572 0.9995446
times_uncensored_sampled[48] 149.3557434 392.1194164 3950.273 0.9993770
times_uncensored_sampled[49] 150.1410749 407.3674207 4252.396 1.0001112
times_uncensored_sampled[50] 148.1476298 400.1815493 4008.845 0.9997435
times_uncensored_sampled[51] 158.0171865 412.0580278 3812.416 0.9995659
times_uncensored_sampled[52] 268.4095033 724.2534684 4103.208 1.0005161
times_uncensored_sampled[53] 260.7542578 694.6795417 3598.143 1.0004337
times_uncensored_sampled[54] 151.9848081 413.1646537 4053.151 1.0004361
times_uncensored_sampled[55] 274.0093841 710.5150390 3822.664 1.0001850
times_uncensored_sampled[56] 157.7603873 401.1848804 3604.868 0.9995065
times_uncensored_sampled[57] 158.8154533 394.2687233 3892.528 0.9996226
times_uncensored_sampled[58] 273.0285941 713.2958126 3840.130 1.0000100
times_uncensored_sampled[59] 277.7729494 705.4068810 3837.050 0.9998800
times_uncensored_sampled[60] 158.5445373 413.6319976 3827.185 0.9998950
times_uncensored_sampled[61] 155.3038762 418.5809072 4293.174 0.9998075
times_uncensored_sampled[62] 270.9538354 675.3483397 3585.639 0.9997213
times_uncensored_sampled[63] 153.4892651 394.2983141 3873.225 0.9998757
times_uncensored_sampled[64] 269.8857269 719.7626162 4154.620 1.0000748
times_uncensored_sampled[65] 267.9545624 727.6245631 3820.194 1.0000304
times_uncensored_sampled[66] 147.3273563 393.0093472 4103.403 1.0012920
times_uncensored_sampled[67] 155.6510899 426.0679237 3885.663 0.9997231
times_uncensored_sampled[68] 266.1606632 714.5464129 4209.772 0.9995455
times_uncensored_sampled[69] 154.4216450 402.0354154 3607.432 1.0000290
times_uncensored_sampled[70] 267.9461842 733.2750212 3940.083 1.0006691
times_uncensored_sampled[71] 149.7275470 398.0438588 4098.204 0.9996294
times_uncensored_sampled[72] 272.9121811 702.6464381 4178.919 0.9997263
times_uncensored_sampled[73] 149.2066784 393.9660371 3893.169 1.0001871
times_uncensored_sampled[74] 152.1345708 402.6867815 4127.564 0.9998100
times_uncensored_sampled[75] 155.1919939 402.8931911 4174.004 0.9997879
times_uncensored_sampled[76] 152.3158555 417.5904198 4150.652 0.9993528
times_uncensored_sampled[77] 152.0095113 422.9333956 3951.322 1.0003735
times_uncensored_sampled[78] 152.9087824 406.1686336 3927.466 0.9993340
times_uncensored_sampled[79] 154.7979102 404.3355464 3862.676 0.9996636
times_uncensored_sampled[80] 152.4638511 406.4967408 3844.484 1.0001029
times_uncensored_sampled[81] 148.7349301 395.4800443 3913.585 0.9995614
times_uncensored_sampled[82] 154.3292706 411.5500956 3937.748 1.0018439
times_uncensored_sampled[83] 153.6951043 405.2220533 3661.909 0.9992773
times_uncensored_sampled[84] 154.0888605 393.4428255 3912.175 0.9999959
times_uncensored_sampled[85] 156.3750849 407.9943137 4169.247 0.9997303
times_uncensored_sampled[86] 263.9198422 724.6817056 3871.126 0.9991486
times_uncensored_sampled[87] 149.9423046 390.0358502 3693.431 1.0002463
times_uncensored_sampled[88] 274.7452547 725.0482546 3850.503 1.0014153
times_uncensored_sampled[89] 156.9869259 404.0217356 4105.911 0.9998903
times_uncensored_sampled[90] 155.0585469 407.8482453 4038.496 1.0000828
times_uncensored_sampled[91] 271.0668398 738.1384308 4103.721 0.9999729
times_uncensored_sampled[92] 147.9347481 405.5016718 4030.078 1.0018519
times_uncensored_sampled[93] 272.0893941 709.1744071 3889.964 0.9996824
times_uncensored_sampled[94] 153.9761259 402.6573501 4036.034 0.9993945
times_uncensored_sampled[95] 152.8090047 417.6485294 4074.651 1.0005958
times_uncensored_sampled[96] 155.7092041 409.2277591 4097.788 0.9999564
times_uncensored_sampled[97] 151.4021571 399.2501981 3852.400 0.9998574
times_uncensored_sampled[98] 155.4155118 435.3941544 4213.659 0.9999416
[ reached getOption("max.print") -- omitted 547 rows ]
exp_surv_model_draws <- tidybayes::tidy_draws(exp_surv_model_fit)
exp_surv_model_draws